Timely and accurate radionuclide detection and identification is a critical capability and first line defense against the transportation of nuclear contraband and potential terrorist threats. However, radionuclide detection can be difficult due to low-count gamma emissions produced when, for example, sources are shielded to disguise their existence or are being transported and in relative motion with respect to the sensors. Moreover, background noise, finite detector resolution, and the heterogeneous media along transport paths between the sources and detectors all compound the measurement difficulty by “burying” the low-count emissions in the background and Compton scattering noise.
Various signal processing tools, techniques, and algorithms are known for detecting and identifying radionuclides from their gamma ray emission signatures produced in a gamma ray energy (probability) distribution or spectrum created as an energy histogram of measured arrival data at various energy levels (count vs. binned energy). In particular, these gamma spectroscopy methods analyze and interpret the energy spectrum to find emissions signatures (“energy lines”) uniquely characterizing individual radionuclides. However, these techniques have been known to fail on low-count measurement data, revealing inherent problems in both (a) the underlying physics models of the radiation emissions, transport, and measurement processes used for signal processing, and (2) the underlying mathematical framework for solving the detection problem.
For example, contemporary approaches to radionuclide detection utilize linearized approximations to the nonlinear processors (extended and unscented Kalman filters) which imply underlying Gaussian probabilistic assumptions. Unfortunately the nuclear physics dominating this problem is not characterized by unimodal (one peak) distributions, but rather by multimodal (multiple peaks) representations. Moreover, existing methodologies are based on enhancing the output gamma-ray spectrum by attempting to deconvolve the propagation uncertainties that contaminate the spectrum, i.e. remove background interference and noise, so as to increase the probability of extracting the spectral (energy) lines and identifying the radionuclide of interest. However, it is often the case when used in practice that the low-count detection limitations of contemporary gamma ray spectrometry tools and methods reveal the reliance of the underlying analysis algorithms upon heuristic approaches based upon the experience of analyst, even requiring in some cases the intervention of a trained practitioner to analyze the results and guide the interpretation process. In a terrorist type scenario, this is not acceptable, since timely and accurate performance is imperative. There is therefore a need for a new technique and methodology that provides for more timely, sensitive, and accurate detection of radionuclides that pose a significant terrorist threat.